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>>> 星瞳科技-OpenMV中文教程网 <<<
本例程为07-Face_Detection-face_detection.py
本例程的目的是利用haar算子实现人脸识别。
# Face Detection Example # # This example shows off the built-in face detection feature of the OpenMV Cam. # # Face detection works by using the Haar Cascade feature detector on an image. A # Haar Cascade is a series of simple area contrasts checks. For the built-in # frontalface detector there are 25 stages of checks with each stage having # hundreds of checks a piece. Haar Cascades run fast because later stages are # only evaluated if previous stages pass. Additionally, your OpenMV Cam uses # a data structure called the integral image to quickly execute each area # contrast check in constant time (the reason for feature detection being # grayscale only is because of the space requirment for the integral image). import sensor, time, image # Reset sensor sensor.reset() # Sensor settings sensor.set_contrast(1) sensor.set_gainceiling(16) # HQVGA and GRAYSCALE are the best for face tracking. sensor.set_framesize(sensor.HQVGA) sensor.set_pixformat(sensor.GRAYSCALE) #注意人脸识别只能用灰度图哦 # Load Haar Cascade # By default this will use all stages, lower satges is faster but less accurate. face_cascade = image.HaarCascade("frontalface", stages=25) #image.HaarCascade(path, stages=Auto)加载一个haar模型。haar模型是二进制文件, #这个模型如果是自定义的,则引号内为模型文件的路径;也可以使用内置的haar模型, #比如“frontalface” 人脸模型或者“eye”人眼模型。 #stages值未传入时使用默认的stages。stages值设置的小一些可以加速匹配,但会降低准确率。 print(face_cascade) # FPS clock clock = time.clock() while (True): clock.tick() # Capture snapshot img = sensor.snapshot() # Find objects. # Note: Lower scale factor scales-down the image more and detects smaller objects. # Higher threshold results in a higher detection rate, with more false positives. objects = img.find_features(face_cascade, threshold=0.75, scale=1.35) #image.find_features(cascade, threshold=0.5, scale=1.5),thresholds越大, #匹配速度越快,错误率也会上升。scale可以缩放被匹配特征的大小。 #在找到的目标上画框,标记出来 # Draw objects for r in objects: img.draw_rectangle(r) # Print FPS. # Note: Actual FPS is higher, streaming the FB makes it slower. print(clock.fps())
可以直接用openCv+PYTHON 的程序,实现识别视频中的人脸吗?